Add reference tests on VGG-19 (#11)

* fix gradient methods not using the Flux model in test-mode
* add reference tests on VGG-19 with random weights, therefore not requiring large downloads of parameters
* remove test dependency on Metalhead
* update CI configuration

Author: adrhill

.github/workflows/ci.yml

@@ -1,7 +1,11 @@
 name: CI
 on:
-  - push
-  - pull_request
+  pull_request:
+    branches:
+      - master
+  push:
+    branches:
+      - master
 jobs:
   test:
     name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}

Project.toml

@@ -15,18 +15,15 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 ColorSchemes = "3"
 Flux = "0.12"
 ImageCore = "0.8, 0.9"
-Metalhead = "0.6"
+JLD2 = "0.4"
 Zygote = "0.6"
 julia = "1.6"
 
 [extras]
-ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
-Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
-Metalhead = "dbeba491-748d-5e0e-a39e-b530a07fa0cc"
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReferenceTests = "324d217c-45ce-50fc-942e-d289b448e8cf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
 
 [targets]
-test = ["ImageMagick", "Images", "Metalhead", "Random", "ReferenceTests", "Test", "TestImages"]
+test = ["JLD2", "Random", "ReferenceTests", "Test"]

src/gradient.jl

@@ -1,17 +1,17 @@
 struct Gradient{C<:Chain} <: AbstractXAIMethod
     model::C
-    Gradient(model::Chain) = new{typeof(model)}(check_ouput_softmax(model))
+    Gradient(model::Chain) = new{typeof(model)}(Flux.testmode!(check_ouput_softmax(model)))
 end
-function (method::Gradient)(input, ns::AbstractNeuronSelector)
-    output = method.model(input)
+function (analyzer::Gradient)(input, ns::AbstractNeuronSelector)
+    output = analyzer.model(input)
     output_neuron = ns(output)
-    expl = gradient((in) -> method.model(in)[output_neuron], input)[1]
+    expl = gradient((in) -> analyzer.model(in)[output_neuron], input)[1]
     return expl, output
 end
 
 struct InputTimesGradient{C<:Chain} <: AbstractXAIMethod
     model::C
-    InputTimesGradient(model::Chain) = new{typeof(model)}(check_ouput_softmax(model))
+    InputTimesGradient(model::Chain) = new{typeof(model)}(Flux.testmode!(check_ouput_softmax(model)))
 end
 function (analyzer::InputTimesGradient)(input, ns::AbstractNeuronSelector)
     output = analyzer.model(input)